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Compared to earlier multistage frameworks using CNN features, recentend-to-end deep approaches for fine-grained recognition essentially enhance themid-level learning capability of CNNs. Previous approaches achieve this byintroducing an auxiliary network to infuse localization information into themain classification network, or a sophisticated feature encoding method tocapture higher order feature statistics. We show that mid-level representationlearning can be enhanced within the CNN framework, by learning a bank ofconvolutional filters that capture class-specific discriminative patcheswithout extra part or bounding box annotations. Such a filter bank is wellstructured, properly initialized and discriminatively learned through a novelasymmetric multi-stream architecture with convolutional filter supervision anda non-random layer initialization. Experimental results show that our approachachieves state-of-the-art on three publicly available fine-grained recognitiondatasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft). Ablation studies andvisualizations are provided to understand our approach.

Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition